Global Mean Temperature Change Research Articles

Widening of the Hadley Cell from Last Glacial Maximum to Future Climate

Abstract The Hadley cell (HC) change from paleoclimate to future climate is examined by comparing coupled model simulations archived for the Paleoclimate Modeling Intercomparison Project phase 3 (PMIP3) and phase 5 of the Coupled Model Intercomparison Project (CMIP5). Specifically, HC width and strength are evaluated using 100-yr equilibrium simulations for the Last Glacial Maximum (LGM), preindustrial (PI), and extended concentration pathway 4.5 (ECP4.5) conditions. Where available, ECP8.5 simulations are also examined to increase the sample size. All models show a systematic widening of the HC from the LGM to the PI and to the ECP4.5 and ECP8.5 simulations. Such widening, which is found in both hemispheres with more robust change in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH), is significantly correlated with global-mean surface air temperature change and the associated static stability change in the subtropics. Based on the zero-crossing latitude of 500-hPa mass streamfunction, about 4.5° latitude widening of the HC results from global warming of 10°C. HC strength also exhibits a systematic weakening in the NH. However, in the SH, HC strength shows a rather minor change from LGM to ECP4.5 conditions because of the cancellation between HC weakening during the austral summer–fall and its strengthening during the spring. This result, which suggests no systematic relationship between HC width and strength changes, is discussed in the context of quasigeostrophic zonal-mean dynamics. Overall findings are also compared with recent studies that are based on transient climate model simulations.

Future extreme climate changes linked to global warming intensity

Based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) daily dataset, we investigate changes of the terrestrial extreme climates given that the global mean temperature increases persistently under the Representative Concentration Pathways 8.5 (RCP8.5) scenario. Compared to preindustrial conditions, more statistically significant extreme temperatures, precipitations, and dry spells are expected in the 21st century. Cold extremes decrease and warm extremes increase in a warmer world, and cold extremes tend to be more sensitive to global warming than the warm ones. When the global mean temperature increases, cold nights, cold days, and warm nights all display nonlinear relationships with it, such as the weakening of the link projected after 3 °C global warming, while the other indices generally exhibit differently, with linear relationships. Additionally, the relative changes in the indices related to extreme precipitation show significantly consistent linear changes with the global warming magnitude. Compared with the precipitation extremes, changes in temperature extremes are more strongly related to the global mean temperature changes. For the projection of the extreme precipitation changes, models show higher uncertainty than that in extreme temperature changes, and the uncertainty for the precipitation extremes becomes more remarkable when the global warming exceeds 5 °C.

Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework

Abstract. This article extends a previous study Seneviratne et al. (2016) to provide regional analyses of changes in climate extremes as a function of projected changes in global mean temperature. We introduce the DROUGHT-HEAT Regional Climate Atlas, an interactive tool to analyse and display a range of well-established climate extremes and water-cycle indices and their changes as a function of global warming. These projections are based on simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). A selection of example results are presented here, but users can visualize specific indices of interest using the online tool. This implementation enables a direct assessment of regional climate changes associated with global mean temperature targets, such as the 2 and 1.5° limits agreed within the 2015 Paris Agreement.

Comparing Climate Sensitivity, Past and Present.

Climate sensitivity represents the global mean temperature change caused by changes in the radiative balance of climate; it is studied for both present/future (actuo) and past (paleo) climate variations, with the former based on instrumental records and/or various types of model simulations. Paleo-estimates are often considered informative for assessments of actuo-climate change caused by anthropogenic greenhouse forcing, but this utility remains debated because of concerns about the impacts of uncertainties, assumptions, and incomplete knowledge about controlling mechanisms in the dynamic climate system, with its multiple interacting feedbacks and their potential dependence on the climate background state. This is exacerbated by the need to assess actuo- and paleoclimate sensitivity over different timescales, with different drivers, and with different (data and/or model) limitations. Here, we visualize these impacts with idealized representations that graphically illustrate the nature of time-dependent actuo- and paleoclimate sensitivity estimates, evaluating the strengths, weaknesses, agreements, and differences of the two approaches. We also highlight priorities for future research to improve the use of paleo-estimates in evaluations of current climate change.

Halfway to doubling of CO2 radiative forcing

The “double CO2” experiment has become a standard experiment in climate science, and a convenient way of comparing the sensitivity of different climate models. Double CO2 was first used by Arrhenius in the 19th century and in the classic paper by Manabe and Wetherald, published 50 years ago, which marked the start of the modern era of climate modeling. Doubling CO2 now has an iconic role in climate research. The equilibrium climate sensitivity (ECS) is defined as the global-mean surface temperature change resulting from a doubling of CO2, which is a headline result in Intergovernmental Panel on Climate Change (IPCC) assessments. In its most recent assessment IPCC concluded that the ECS “is likely in the range 1.5 to 4.5oC”. We show that we are now halfway to doubling of CO2 since pre-industrial times in terms of radiative forcing, but not in concentration.

The oceanic origin of path-independent carbon budgets

Virtually all Earth system models (ESM) show a near proportional relationship between cumulative emissions of CO2 and change in global mean temperature, a relationship which is independent of the emissions pathway taken to reach a cumulative emissions total. The relationship, which has been named the Transient Climate Response to Cumulative CO2 Emissions (TCRE), gives rise to the concept of a ‘carbon budget’. That is, a finite amount of carbon that can be burnt whilst remaining below some chosen global temperature change threshold, such as the 2.0 °C target set by the Paris Agreement. Here we show that the path-independence of TCRE arises from the partitioning ratio of anthropogenic carbon between the ocean and the atmosphere being almost the same as the partitioning ratio of enhanced radiative forcing between the ocean and space. That these ratios are so close in value is a coincidence unique to CO2. The simple model used here is underlain by many assumptions and simplifications but does reproduce key aspects of the climate system relevant to the path-independence of carbon budgets. Our results place TCRE and carbon budgets on firm physical foundations and therefore help validate the use of these metrics for climate policy.

The use of climate information to estimate future mortality from high ambient temperature: A systematic literature review.

Background and objectivesHeat related mortality is of great concern for public health, and estimates of future mortality under a warming climate are important for planning of resources and possible adaptation measures. Papers providing projections of future heat-related mortality were critically reviewed with a focus on the use of climate model data. Some best practice guidelines are proposed for future research.MethodsThe electronic databases Web of Science and PubMed/Medline were searched for papers containing a quantitative estimate of future heat-related mortality. The search was limited to papers published in English in peer-reviewed journals up to the end of March 2017. Reference lists of relevant papers and the citing literature were also examined. The wide range of locations studied and climate data used prevented a meta-analysis.ResultsA total of 608 articles were identified after removal of duplicate entries, of which 63 were found to contain a quantitative estimate of future mortality from hot days or heat waves. A wide range of mortality models and climate model data have been used to estimate future mortality. Temperatures in the climate simulations used in these studies were projected to increase. Consequently, all the papers indicated that mortality from high temperatures would increase under a warming climate. The spread in projections of future climate by models adds substantial uncertainty to estimates of future heat-related mortality. However, many studies either did not consider this source of uncertainty, or only used results from a small number of climate models. Other studies showed that uncertainty from changes in populations and demographics, and the methods for adaptation to warmer temperatures were at least as important as climate model uncertainty. Some inconsistencies in the use of climate data (for example, using global mean temperature changes instead of changes for specific locations) and interpretation of the effects on mortality were apparent. Some factors which have not been considered when estimating future mortality are summarised.ConclusionsMost studies have used climate data generated using scenarios with medium and high emissions of greenhouse gases. More estimates of future mortality using climate information from the mitigation scenario RCP2.6 are needed, as this scenario is the only one under which the Paris Agreement to limit global warming to 2°C or less could be realised. Many of the methods used to combine modelled data with local climate observations are simplistic. Quantile-based methods might offer an improved approach, especially for temperatures at the ends of the distributions. The modelling of adaptation to warmer temperatures in mortality models is generally arbitrary and simplistic, and more research is needed to better quantify adaptation. Only a small number of studies included possible changes in population and demographics in their estimates of future mortality, meaning many estimates of mortality could be biased low. Uncertainty originating from establishing a mortality baseline, climate projections, adaptation and population changes is important and should be considered when estimating future mortality.

Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models

Abstract. Pattern scaling is a well-established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare two methods of pattern scaling for annual mean precipitation (regression and epoch difference) and evaluate which method is better in particular circumstances by quantifying their robustness to interpolation/extrapolation in time, inter-model variations, and inter-scenario variations. Both the regression and epoch-difference methods (the two most commonly used methods of pattern scaling) have good absolute performance in reconstructing the climate model output, measured as an area-weighted root mean square error. We decompose the precipitation response in the RCP8.5 scenario into a CO2 portion and a non-CO2 portion. Extrapolating RCP8.5 patterns to reconstruct precipitation change in the RCP2.6 scenario results in large errors due to violations of pattern scaling assumptions when this CO2-/non-CO2-forcing decomposition is applied. The methodologies discussed in this paper can help provide precipitation fields to be utilized in other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.

Extraction of Glacial Lakes in Gangotri Glacier Using Object-Based Image Analysis

Due to change in global mean temperature, most Himalayan glaciers have shown retreat, resulting in an increase in the number and size of glacial lakes, which may give rise to glacial lake outburst flood event. These glacial lakes typically grow from small supraglacial lakes (SGLs). Therefore, it is important to map and monitor the SGLs on a regular basis. Most of the studies have utilized medium to coarse resolution images for the extraction of glacial lakes. With the availability of high spatial resolution data, it has also become possible to map small glacial lakes. The literature suggests some studies on the use of high resolution data to map glacial lakes. However, the extraction is majorly based on simple to apply pixel-based classification methods, which results into high misclassification thereby increasing the task of manual post processing. In this study, we used a novel approach, known as object-based image analysis (OBIA), for the mapping of SGLs. A new index has also been proposed for classification of SGLs. As a case study, the SGLs of Gangotri glacier (Uttarakhand Himalayas) have been mapped from the high spatial resolution data of LISS-IV using the proposed OBIA approach. The results have been compared with those obtained from object-based normalized difference water index (NDWI) and pixel-based mapping methods. The validation of the SGLs boundaries has been carried out with respect to the manually digitized database of SGLs. A significant increase in accuracy of the mapping has been observed over the benchmarked traditional methods.

IPCC reasons for concern regarding climate change risks

The reasons for concern framework communicates scientific understanding about risks in relation to varying levels of climate change. The framework, now a cornerstone of the IPCC assessments, aggregates global risks into five categories as a function of global mean temperature change. We review the framework's conceptual basis and the risk judgments made in the most recent IPCC report, confirming those judgments in most cases in the light of more recent literature and identifying their limitations. We point to extensions of the framework that offer complementary climate change metrics to global mean temperature change and better account for possible changes in social and ecological system vulnerability. Further research should systematically evaluate risks under alternative scenarios of future climatic and societal conditions.

Inferring Aerosol Cooling from Hydrological Sensitivity

Abstract Hydrological sensitivity is the change in global-mean precipitation per degree of global-mean temperature change. This paper shows that the hydrological sensitivity of the response to anthropogenic aerosol forcing is distinct from that of the combined response to all other forcings and that this difference is sufficient to infer the associated cooling in global-mean temperature. This result is demonstrated using temperature and precipitation data generated by climate models and is robust across different climate models. Remarkably, greenhouse gas warming and aerosol cooling can be estimated in a model without using any spatial or temporal gradient information in the response, provided temperature data are augmented by precipitation data. Over the late twentieth century, the hydrological sensitivities of climate models differ significantly from that of observations. Whether this discrepancy can be attributed to observational error, which is substantial as different estimates of global-mean precipitation are not even significantly correlated with each other, or to model error is unclear. The results highlight the urgency to construct accurate estimates of global precipitation from past observations and for reducing model uncertainty in hydrological sensitivity. This paper also clarifies that previous estimates of hydrological sensitivity are limited in that standard regression methods neglect temperature–precipitation relations that occur through internal variability. An alternative method for estimating hydrological sensitivity that overcomes this limitation is presented.

Geoengineering as a design problem

Abstract. Understanding the climate impacts of solar geoengineering is essential for evaluating its benefits and risks. Most previous simulations have prescribed a particular strategy and evaluated its modeled effects. Here we turn this approach around by first choosing example climate objectives and then designing a strategy to meet those objectives in climate models. There are four essential criteria for designing a strategy: (i) an explicit specification of the objectives, (ii) defining what climate forcing agents to modify so the objectives are met, (iii) a method for managing uncertainties, and (iv) independent verification of the strategy in an evaluation model. We demonstrate this design perspective through two multi-objective examples. First, changes in Arctic temperature and the position of tropical precipitation due to CO2 increases are offset by adjusting high-latitude insolation in each hemisphere independently. Second, three different latitude-dependent patterns of insolation are modified to offset CO2-induced changes in global mean temperature, interhemispheric temperature asymmetry, and the Equator-to-pole temperature gradient. In both examples, the "design" and "evaluation" models are state-of-the-art fully coupled atmosphere–ocean general circulation models.

Exploring the Effects of Solar Radiation Management on Water Cycling in a Coupled Land–Atmosphere Model*

Abstract Solar radiation management (SRM) has been proposed as a form of geoengineering to reduce the climate effects of anthropogenic greenhouse gas emissions. Modeling studies have concluded that SRM, through a reduction in total solar irradiance by approximately 2%, roughly compensates for global mean temperature changes from a doubling of carbon dioxide concentrations. This paper examines the impact of SRM on the terrestrial hydrologic cycle using the Community Land Model, version 4, coupled to the Community Atmosphere Model, version 4, with reductions in solar radiation relative to simulations with present-day and elevated CO2 concentrations. There are significant global and regional impacts due to vegetation–climate interactions that are not compensated when reductions in total solar irradiance of 1%, 2%, and 3% are imposed on top of a doubling of present-day CO2 concentrations. Water cycling slows down under SRM, including decreases in global mean precipitation and evapotranspiration. Changes in runoff and soil moisture are spatially and temporally variable, with implications for local water availability. In the tropics, evapotranspiration decreases because of increases in vegetation water use efficiency. In northern midlatitudes, soil moisture increases when evapotranspiration decreases, with some exceptions during boreal summer. Changes in soil evaporation influence water cycling in the southern subtropics, rather than changes in transpiration. The hydrologic response to SRM is nonlinear, with global mean decreases greater than expected. These results imply that SRM may not compensate for higher greenhouse gas concentrations when one considers land–atmosphere interactions.

The Global Warming–Induced South Asian High Change and Its Uncertainty

Abstract Based on models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), the present study investigates the South Asian high (SAH) change in response to global warming. Under global warming, the selected 16 coupled general circulation models all feature an elevation of geopotential height at 100 hPa to the south of the SAH climatological position; an easterly response is found over the northern Indian Ocean in all the models, while a westerly response is found over subtropical Asia. The ridges of the SAH shift equatorward in 75% of models. Using the linear baroclinic model, it is found that the combined effects of latent heating and the mean advection of stratification change (MASC) are mainly responsible for those responses. The MASC mainly leads to the aforementioned easterly and westerly responses; the latent heating contributes to the geopotential height response and the easterly response over the northern Indian Ocean. The most important intermodel diversity is found in the 100-hPa circulation change under global warming, accounting for more than half of the total intermodel variance. The intermodel spread of latent heating and the MASC are important factors in driving the 100-hPa circulation diversity. Furthermore, analysis shows that the projected uncertainties in humidity, vertical velocity, and global mean temperature change are the three most important sources of intermodel diversity for the 100-hPa circulation change.

Time-dependent global warming impact of tree stump bioenergy in Sweden

Tree stump harvesting could significantly increase the amount of bioenergy feedstock that forestry can supply to substitute for fossil alternatives. However, the climate mitigation potential of using stumps for bioenergy has been debated due to their often long residence time in the forest caused by slow decomposition. This study evaluated the climate effect over time of utilising stumps for bioenergy using ecosystem forest carbon modelling and time-dependent LCA methodology, including uncertainties in soil carbon changes. Different climate impact metrics were used (global mean temperature change, global warming potential and cumulative radiative forcing) and evaluations were made for single harvest as well as continuous supply over a landscape. Stump harvesting scenarios for spruce forests across Sweden were simulated and the forest net carbon balance was estimated as the difference compared with a reference scenario where the stumps were left to decompose in the forest.The results showed that using stump residues from commercial forestry in Sweden gives a climate benefit when they substitute for fossil fuel, even in a shorter perspective of around two decades. The temperature impact from using stumps for bioenergy at the stand level peaked after 10–15years and then declined steadily to ∼15% of the maximum level during the following 4–5 decades. The remaining long-term climate impact was small compared to using fossil fuel. An immediate climate benefit was achieved when replacing fossil coal, whereas the parity time, i.e. the time to reach climate benefit was 12–16years (±2years) when replacing natural gas, depending on geographical location. For continuous supply of stump bioenergy over a landscape, the corresponding parity time was 22–28years. There was a higher impact on global climate for northern Sweden, although the absolute difference was small. Sensitivity analysis indicated a moderate additional climate warming effect from the soil disturbance caused by stump harvesting.

Small global-mean cooling due to volcanic radiative forcing

In both the observational record and atmosphere-ocean general circulation model (AOGCM) simulations of the last $$\sim$$ 150 years, short-lived negative radiative forcing due to volcanic aerosol, following explosive eruptions, causes sudden global-mean cooling of up to $$\sim$$ 0.3 K. This is about five times smaller than expected from the transient climate response parameter (TCRP, K of global-mean surface air temperature change per W m−2 of radiative forcing increase) evaluated under atmospheric CO2 concentration increasing at 1 % yr−1. Using the step model (Good et al. in Geophys Res Lett 38:L01703, 2011. doi: 10.1029/2010GL045208 ), we confirm the previous finding (Held et al. in J Clim 23:2418–2427, 2010. doi: 10.1175/2009JCLI3466.1 ) that the main reason for the discrepancy is the damping of the response to short-lived forcing by the thermal inertia of the upper ocean. Although the step model includes this effect, it still overestimates the volcanic cooling simulated by AOGCMs by about 60 %. We show that this remaining discrepancy can be explained by the magnitude of the volcanic forcing, which may be smaller in AOGCMs (by 30 % for the HadCM3 AOGCM) than in off-line calculations that do not account for rapid cloud adjustment, and the climate sensitivity parameter, which may be smaller than for increasing CO2 (40 % smaller than for 4 × CO2 in HadCM3).

The Impacts of Land-Use and Land-Cover Change on Tropospheric Temperatures at Global and Regional Scales

Abstract The impacts of land-use and land-cover change (LULCC) on tropospheric temperatures are investigated in this study using the fully coupled Community Earth System Model. Two simulations are performed using potential and current vegetation cover. The results show that LULCC can induce detectable changes in the tropospheric air temperature. Although the influence of LULCC on tropospheric temperature is weak, a significant influence can still be found below 300 hPa in summer over land. Compared to the global-mean temperature change, LULCC-induced changes in the regional-mean air temperature can be 2–3 times larger in the middle–upper troposphere and approximately 8 times larger in the lower troposphere. In East Asia and South Asia, LULCC is shown to produce significant decreases (0.2° to 0.4°C) in air temperature in the middle–upper troposphere in spring and autumn due to the largest decrease in the latent heat release from precipitation. In Europe and North America, the most significant tropospheric cooling occurs in summer, which can be attributed to the significant decrease in the absorbed solar radiation and sensible heat flux during this season. In addition to local effects, LULCC also induces nonlocal responses in the tropospheric air temperature that are characterized by significant decreases over the leeward sides of LULCC regions, which include East Asia–western North Pacific Ocean, Mediterranean Sea–North Africa, North America–Atlantic Ocean, and North America–eastern Pacific. Cooling in the leeward sides of LULCC regions is primarily caused by an enhanced cold advection induced by LULCC.

Climate model emulation in an integrated assessment framework: a case study for mitigation policies in the electricity sector

Abstract. We present a carbon-cycle–climate modelling framework using model emulation, designed for integrated assessment modelling, which introduces a new emulator of the carbon cycle (GENIEem). We demonstrate that GENIEem successfully reproduces the CO2 concentrations of the Representative Concentration Pathways when forced with the corresponding CO2 emissions and non-CO2 forcing. To demonstrate its application as part of the integrated assessment framework, we use GENIEem along with an emulator of the climate (PLASIM-ENTSem) to evaluate global CO2 concentration levels and spatial temperature and precipitation response patterns resulting from CO2 emission scenarios. These scenarios are modelled using a macroeconometric model (E3MG) coupled to a model of technology substitution dynamics (FTT), and represent different emissions reduction policies applied solely in the electricity sector, without mitigation in the rest of the economy. The effect of cascading uncertainty is apparent, but despite uncertainties, it is clear that in all scenarios, global mean temperatures in excess of 2 °C above pre-industrial levels are projected by the end of the century. Our approach also highlights the regional temperature and precipitation patterns associated with the global mean temperature change occurring in these scenarios, enabling more robust impacts modelling and emphasizing the necessity of focusing on spatial patterns in addition to global mean temperature change.

CO2 over the past 5 million years: Continuous simulation and new δ11B-based proxy data

During the past five million yrs, benthic δ18O records indicate a large range of climates, from warmer than today during the Pliocene Warm Period to considerably colder during glacials. Antarctic ice cores have revealed Pleistocene glacial–interglacial CO2 variability of 60–100 ppm, while sea level fluctuations of typically 125 m are documented by proxy data. However, in the pre-ice core period, CO2 and sea level proxy data are scarce and there is disagreement between different proxies and different records of the same proxy. This hampers comprehensive understanding of the long-term relations between CO2, sea level and climate. Here, we drive a coupled climate–ice sheet model over the past five million years, inversely forced by a stacked benthic δ18O record. We obtain continuous simulations of benthic δ18O, sea level and CO2 that are mutually consistent. Our model shows CO2 concentrations of 300 to 470 ppm during the Early Pliocene. Furthermore, we simulate strong CO2 variability during the Pliocene and Early Pleistocene. These features are broadly supported by existing and new δ11B-based proxy CO2 data, but less by alkenone-based records. The simulated concentrations and variations therein are larger than expected from global mean temperature changes. Our findings thus suggest a smaller Earth System Sensitivity than previously thought. This is explained by a more restricted role of land ice variability in the Pliocene. The largest uncertainty in our simulation arises from the mass balance formulation of East Antarctica, which governs the variability in sea level, but only modestly affects the modeled CO2 concentrations.

Predicting future uncertainty constraints on global warming projections.

Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by “current knowledge” of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.